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BackgroundCachexia is a multifactorial metabolic syndrome with high morbidity and mortality in patients with advanced cancer. The diagnosis of cancer cachexia depends on objective measures of clinical symptoms and a history of weight loss, which lag behind disease progression and have limited utility for the early diagnosis of cancer cachexia. In this study, we performed a nuclear magnetic resonance‐based metabolomics analysis to reveal the metabolic profile of cancer cachexia and establish a diagnostic model.MethodsEighty‐four cancer cachexia patients, 33 pre‐cachectic patients, 105 weight‐stable cancer patients, and 74 healthy controls were included in the training and validation sets. Comparative analysis was used to elucidate the distinct metabolites of cancer cachexia, while metabolic pathway analysis was employed to elucidate reprogramming pathways. Random forest, logistic regression, and receiver operating characteristic analyses were used to select and validate the biomarker metabolites and establish a diagnostic model.ResultsForty‐six cancer cachexia patients, 22 pre‐cachectic patients, 68 weight‐stable cancer patients, and 48 healthy controls were included in the training set, and 38 cancer cachexia patients, 11 pre‐cachectic patients, 37 weight‐stable cancer patients, and 26 healthy controls were included in the validation set. All four groups were age‐matched and sex‐matched in the training set. Metabolomics analysis showed a clear separation of the four groups. Overall, 45 metabolites and 18 metabolic pathways were associated with cancer cachexia. Using random forest analysis, 15 of these metabolites were identified as highly discriminating between disease states. Logistic regression and receiver operating characteristic analyses were used to create a distinct diagnostic model with an area under the curve of 0.991 based on three metabolites. The diagnostic equation was Logit(P) = −400.53 – 481.88 × log(Carnosine) −239.02 × log(Leucine) + 383.92 × log(Phenyl acetate), and the result showed 94.64% accuracy in the validation set.ConclusionsThis metabolomics study revealed a distinct metabolic profile of cancer cachexia and established and validated a diagnostic model. This research provided a feasible diagnostic tool for identifying at‐risk populations through the detection of serum metabolites.

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Purpose: Extensive research has reported that the tumor microenvironment components play crucial roles in tumor progression. Thus, blocking the supports of tumor microenvironment is a promising approach to prevent cancer progression. We aimed to determine whether blocking extracellular ATP-P2RY2 axis could be a potential therapeutic approach for PDAC treatment. Experimental Design: Expression of P2RY2 was determined in 264 human PDAC samples and correlated to patient survival. P2RY2 was inhibited in human PDAC cell lines by antagonist and shRNA, respectively, and cell viability, clonogenicity, and glycolysis were determined. RNA sequencing of PDAC cell line was applied to reveal underlying molecular mechanisms. Multiple PDAC mouse models were used to assess the effects of the P2RY2 inhibition on PDAC progression. Results: P2RY2 was upregulated and associated with poor prognosis in PDAC. Activated P2RY2 by increased extracel-lular ATP in tumor microenvironment promoted PDAC growth and glycolysis. Further studies showed that the agonist-activated P2RY2 triggered PI3K/AKT-mTOR signaling by crosstalk with PDGFR mediated by Yes1, resulting in elevated expression of c-Myc and HIF1a, which subsequently enhanced cancer cell glycolysis. Genetic and pharmacologic inhibition of P2RY2 impaired tumor cell growth in subcutaneous and orthotopic xenograft model, as well as delayed tumor progression in inflammation-driven PDAC model. In addition, synergy was observed when AR-C118925XX, the selective antagonist of P2RY2 receptor, and gemcitabine were combined, resulting in prolonged survival of xenografted PDAC mice. Conclusions: These findings reveal the roles of the P2RY2 in PDAC metabolic reprogramming, suggesting that P2RY2 might be a potential metabolic therapeutic target for PDAC.

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Densities of mixtures of 1-butyl-3-methylimidazolium hexafluorophosphate ([BMIM][PF6]) and 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF4]) with acetonitrile over the entire composition range at T = (293.15 to 343.15) K and with benzene and 1-propanol over the miscible composition range at T = (293.15 to 343.15) K were measured by a vibrating tube densimeter. Density measurements were used to compute the excess molar volumes, V E. The V E values have been fitted to the Redlich−Kister equation. V E values are negative for all the mixtures over the miscible range and become more negative with increasing temperature. The V E values for benzene mixtures are the most negative in the investigation.

The problem of channel quality prediction in cognitive radio networks is investigated in this paper. First, the spectrum sensing process is modeled as a Non-Stationary Hidden Markov Model (NSHMM), which captures the fact that the channel state transition probability is a function of the time interval the primary user has stayed in the current state. Then the model parameters, which carry the information about the expected duration of the channel states and the spectrum sensing accuracy (detection accuracy and false alarm probability) of the SU, are estimated via Bayesian inference with Gibbs sampling. Finally, the estimated NSHMM parameters are employed to design a channel quality metric according to the predicted channel idle duration and spectrum sensing accuracy. Extensive simulation study has been performed to investigate the effectiveness of our design. The results indicate that channel ranking based on the proposed channel quality prediction mechanism captures the idle state duration of the channel and the spectrum sensing accuracy of the SUs, and provides more high quality transmission opportunities and higher successful transmission rates at shorter spectrum waiting times for dynamic spectrum access.